International Journal of Reliability, Risk and Safety: Theory and Application
Volume:4 Issue: 2, Dec 2021

Numerous methods have been introduced to predict the reliability of software. In general, these methods can be divided into two main categories, namely parametric (e.g. software reliability growth models) and non-parametric (e.g. neural networks). Both approaches have been successfully implemented in software testing applications over the past four decades. Since most software reliability prediction data are available in the form of time series, deep recurrent network models (e.g. RNN, LSTM, NARX, and LSTM Encoder-Decoder networks) are considered as powerful tools to be employed in reliability-related problems. However, the problem of overfitting is a major concern when using deep neural networks for software reliability applications. To address this issue, we propose the use of dropout; therefore, this study utilizes a deep learning model based on LSTM Encoder-Decoder Dropout to predict the number of faults in software and assess software reliability. Experimental results show that the proposed model has better prediction performance compared with other RNN-based models.

A thermal sensitivity analysis is performed for an Inverted-F antenna (IFA) at the worst-case thermal condition (reentry phase) of a typical sub-orbital sounding-rocket spaceflight. Electromagnetic simulations show that a 300 Celsius change in the IFA temperature, mainly caused by the aerodynamic heating, increases the reflected power by 1% and decreases the antenna gain by 2.5%. The results show that the degradation of antenna performance and the telemetry link quality is negligible. Therefore, the IFA is a good antenna choice for similar sounding rockets from the thermal reliability point of view.

Cavitation failure is a common failure in bearing shells. Due to the generation and immediate collapse of small gas bubbles, causing high-pressure pulses, the bearing surface is being locally damaged. Cavitation failure is also observed in IC engines due to highly dynamic loading, oscillation of pins, the turbulence of oil flow, and other factors. In this paper, cavitation failure in the crankpin bearing of an IC engine is studied. In order to calculate the bearing lubrication characteristic such as oil fill ratio and maximum oil film pressure, the Elasto-Hydrodynamic Lubrication (EHL) method to consider the effect of stiffness of the bearing shell housing in the model is utilized that incorporates mass conserving algorithms. In order to investigate the effect of some design parameters, such as clearance height between shaft and bearing shell, oil supply temperature and pressure, and oil bore position, on the cavitation failure, a parametric study was also done. The results showed that the cavitation failure in crankpin bearing is not critical and it is slight.

Among all systems, the series system has the lowest optimal replacement time, while the parallel system has the highest optimal replacement time. This paper is comparing the standard age replacement strategy (SARS) with some proposed replacement strategies (strategy A and strategy B) for two multi-unit systems. Two numerical examples are provided for a simple illustration of the proposed replacement cost models under SARS, strategies A and B. The results obtained showed that strategy B can extend the optimal replacement time of a series system.

In this paper, the behavior of detonation waves in a non-ideal environment has been studied. Modeling of detonation has been performed based on one-dimensional Euler equations (momentum conservation) by considering friction as the momentum loss source term in the equation with a single-step Arrhenius law as the chemical kinetics model. Piecewise parabolic method(PPM) has been used to simulate the flow and solve the Euler equations. The shock front conservative tracking algorithm was used to have the finer mesh (Adaptive mesh refinement AMR) at the wave front location. The non-ideal environment is an environment in which external factors, such as friction, cause the detonation behavior to deviate from the ideal behavior. Therefore, the innovation of the present work is modeling detonation in these non-ideal conditions for mixtures with very low activation energy to detect its failure mechanism. The effect of momentum loss on the detonation behavior has been parametrically studied at very low activation energy (in which the detonation behavior is completely regular, here, 8). Depending on the level of mixture activation energy, the detonation has its failure mechanism. It is concluded that the failure mechanisms of the detonation in this study are the mechanisms of pressure drop and chemical reaction rate reduction. The un-burnt packet mechanism is not involved in it. The detonation wave, regardless of the amount of mixture activation energy, fails anyway as the momentum loss exceeds a critical limit.

The present work illustrated the reliability analysis of solar photovoltaic systems and the efficiency of medium grid-connected photovoltaic (PV) power systems with 1-out of- 2 PV panels, one out of one charge controller, 1- out of 3 batteries, 1- out of 2 inverters and one out one Distributor. The units that comprise the solar were studied. Gumbel Hougaard Family Copula method was used to evaluate the performances of solar photovoltaics. Other reliability metrics were investigated, including availability, mean time to failure, and sensitivity analysis. The numerical result was generated using the Maple 13 software. The numerical results were presented in tables, with graphs to go along with them. Failure rates and their effects on various solar photovoltaic subsystems were investigated. Numerical examples are provided to demonstrate the obtained results and to assess the influence of various system characteristics. The current research could aid companies, and their repairers overcome some issues that specific manufacturing and industrial systems repairers face.

In civil and mining industries, Wheel loaders are an important component and their cost capability at effective operation. The environmental and operational factors dramatically affect the performance of loaders. In many cases, failure data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way they can affect the item's reliability are known. However, some factors that may affect the item's reliability are typically unknown and lead to unobserved heterogeneity. These factors are categorized as unobserved covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to erroneous model selection for the time to failure of the item, as well as wrong conclusions and decisions. There is a lack of sufficient knowledge, theoretical background, and a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. The unobserved covariates will be analyzed using frailty models (Such as Mixed Proportional Hazard).A case will illustrate the application of the framework.

With technological advances, companies are allowed to integrate digital data, physical supplies, and human resources, and all this integration capability can be done thanks to Industry 4.0. This concept, also called the fourth industrial revolution, refers to smart companies that work with intelligent cyber-physical systems. Industry 4.0enables automation, data interchange, and big data processing, among others. Then, the process decision-making, efficiency, and productivity improvement for companies will become faster and more accurate, thanks to real-time data processes and all supply chain integration allowed by Industry 4.0. However, the implementation of Industry 4.0 carries several challenges for companies to have success in the transformation of a normal industry into an Industry 4.0, like the necessity of adding new hardware, software, and other technologic devices. Because of this, the implementation and control of Industry 4.0 come with new issues to handle and new failure modes for both hardware and electronic devices. These problems can be faced using reliability engineering tools. Then the object of this research is the use of reliability engineering methodology stress-strength Weibull analysis, highlighting that the behavior of frequency emitted by electronics devices follows a Weibull distribution most of the time. Also, a stress-strength Weibull with a different shape parameter close solution is presented to increase the efficiency and productivity in Industry 4.0 electronic devices.

Condition-based maintenance (CBM) involves making decisions on maintenance based on the actual deterioration conditions of the components. It consists of a chain of states representing various stages of deterioration and a set of maintenance actions. Therefore, condition-based maintenance is a sequential decision-making problem. Reinforcement Learning(RL) is a subfield of Machine Learning proposed for automated decision-making. This article provides an overview of reinforcement learning and deep reinforcement learning methods that have been used so far in condition-based maintenance optimization.

Improving the system's reliability is one way to achieve a secure system. City Gas Station (CGS) has a key role in the timely and safe supply of Natural gas (NG) to residential, commercial, and industrial customers. With complexities inherent in systems, having a proper and all-embracing model of the entirety of a system is not readily possible. The continuous-time Markov chain (CTMC) model is regarded as a great help in communicating, comparing, and integrating partial system models. In this study, we have exploited CTMC for reliability analysis in CGS stations. The CTMC model can solve both time-dependent and stationary state probabilities. Therefore, it can potentially develop the state enumeration method into a sequential one. Implementing this procedure leads to identifying critical components and failure probability, eventually enhancing the station's reliability. Additionally, some suggestions are presented for optimizing the performance of the station.

In many important industries, such as aerial transportation, offshore wind turbine (OWT) structures, and nuclear power plants that reached or are near the end of their useful life, the structural conditions for continued usage are acceptable. Thus, safe continued operation with required modifications and assessment is more cost-effective than replacing them with a new system. To achieve this goal, many studies have been performed on predicting failure time and remaining useful life, especially in systems that require a very high level of reliability. The present review investigates the articles that predict the remaining useful life or failure time in aviation systems, from three perspectives: 1. Methods and algorithms, especially Machine Learning algorithms, which are growing in recent years in the field of Prognosis and Health Management. 2. Historical predictors such as working life history, environmental conditions, mechanical loads, failure records, asset age, maintenance information, or sensor variables and indicators that can be continuously controlled in each system, such as noise, temperature, vibration, and pressure.3. Challenges of researches on prediction of the failure time of flying systems. The literature assessment in this field shows that using diagnostic and prognostic outputs to identify possible defects and their origin, checking the system's health, and predicting the remaining useful life (RUL) is increasing due to market needs.

Today, due to the increase in energy consumption and the increasing use of distributed generation units (DG) such as CHP and fuel cell (FC) units in microgrids, it is necessary to use them in the best possible conditions. The use of distributed generation units, which are mainly installed in small dimensions and on the load side of the distribution network, can provide other benefits, including reducing power transmission losses, increasing production efficiency, and increasing reliability to bring the meantime, the use of electricity and heat simultaneous production units, which are among the most widely used distributed production units, has improved the efficiency of energy production to a great extent by producing electricity and heat simultaneously, thus reducing the costs of energy failure. Therefore, in this paper, the goal is to reduce the cost of production and the cost of lack of energy supply and increase the reliability of the system. In this paper, the working modes of the sample microgrid are evaluated, and finally, the optimization of a multi-objective function, which has goals such as the economical production of each of the distributed production sources, the minimization of the cost of providing the electric and thermal energy of the microgrid, the minimization losses of electrical and thermal energy, load response program and energy storage program. The results of optimal planning of the capacity and number of co-generation units of electricity and heat under different conditions and states show the great effect of using co-generation units of electricity and heat in reducing unsupplied energy and increasing the reliability of the system.